Book/Report

FZJ-2019-01259

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Rekonstruktion von positronen-emissions-tomographischen Bildern unter Einbeziehung anatomischer Information

Lipinski, B. (Corresponding author)FZJ*

1995
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag Jülich

Please use a persistent id in citations: http://hdl.handle.net/2128/21552

Report No.: Juel-3160

Abstract: Positron-Emission-Tomography (PET) is a tool for quantitative imaging metabolie pathways in humans using radioactive tracers. PET images suffer from both low resolution and high statistical noise. Using statistical methods the reconstruction of PET images can be improved by involving high resolution anatomical information obtained from Magnetic Resonance (MR) images. In this work two methods were developed that utilized MR data for PET reconstruction. The anatomical MR information is modeled as apriori distribution of the PET image and combined with the distribution of the measured PET data to generate the aposteriori function from which the Expectation Maximization (EM) type algorithm with a maximum aposteriori (MAP) estimator can be derived. One algorithm (Markov-GEM) uses a Gibbs function to model interactions between neighbouring pixels within the anatomical regions. The other (Gauss-EM) applies a Gauss function with the same mean for all pixels in a given anatomical region. Abasie assumption of these methods is that the radioactivity is homogeneously distributed inside the anatomical regions. Thealgorithms were tested with simulated and phantom data and the results were compared to the conventional reconstruction algorithms, namely the filtered backprojection and the maximum likelihood reconstruction. Further, the algorithms were investigated under the following aspects: count density, object size, missing anatomical information and misregistration of the anatomical information. Compared with the filtered backprojection and the maximum likelihood algorithm the results of both algorithms show a large reduction in noise and a better delineation of borders. Of the two algorithms tested, the Gauss-EM method is superior in noise reduction but suffers on sensitivity to small errors in the a priori information. Regarding missing apriori information or mismatch of the apriori information the Markov-GEM showed greater stability with only small changes in the recovery coefficients.

---

Contributing Institute(s):

1. Publikationen vor 2000 (PRE-2000)

Research Program(s):

1. 899 - ohne Topic (POF3-899) (POF3-899)

---

Database coverage:

---